A lot of the parameterization time is spent on building up the initial guesses for curve fitting. When performing sliding window parameterization, we can potentially speed things up quite a bit by taking advantage of the strong temporal correlations in our data by using the fitted parameters from time-step (t-1) as the guess parameters for the current time window (t).